Washington State Community and Technical Colleges: Faculty ... SBCTC Salary Study.pdf · colleges to recruit and retain high caliber faculty and administrative staff. In this report

Washington State Community and Technical Colleges: Faculty and Administrator Salary Study Update

PREPARED BY THE CENTER FOR ECONOMIC AND BUSINESS RESEARCH Heleana Lally Josh Grandbouche

Allison Rucker Nick Sparling

Lucas Dubois Tyler Murphy

09.04.2018

2 | P a g e State Board for Community and Technical Colleges Salary Study 2018

Conducted by Western Washington University | Center for Economic and Business Research

Table of Contents About the Authors ......................................................................................................................................................... 3

About this Study ............................................................................................................................................................ 3

Peer Groups ................................................................................................................................................................... 4

Methodology ................................................................................................................................................................. 5

Peer System Review of Policies & Practices .................................................................................................................. 5

Cost of Living.................................................................................................................................................................. 6

Part I .......................................................................................................................................................................... 6

Workforce Development Area Average COL Weighted by Population ................................................................. 6

Part II ....................................................................................................................................................................... 12

State Average COL Weighted by Population ....................................................................................................... 12

Faculty Salary ............................................................................................................................................................... 14

Part I ........................................................................................................................................................................ 14

SBCTC Faculty Salaries ......................................................................................................................................... 14

Part II ....................................................................................................................................................................... 16

Peer Group Salaries ............................................................................................................................................. 16

Administrator Salary .................................................................................................................................................... 18

Economic Impact Analysis ........................................................................................................................................... 30

Salary Adjustments by Scenario .............................................................................................................................. 30

Scope of Impact ....................................................................................................................................................... 32

Hard to Hire Factor ...................................................................................................................................................... 35

Methodology ........................................................................................................................................................... 36

Spousal Employment Opportunities .................................................................................................................... 36

Student-to-Teacher Ratio .................................................................................................................................... 37

Distance to Medical Facilities .............................................................................................................................. 38

Retail Diversity ..................................................................................................................................................... 38

Poverty Rate ........................................................................................................................................................ 39

Crime Rate ........................................................................................................................................................... 39

Results ..................................................................................................................................................................... 40

Geographical Information System (GIS) Maps ............................................................................................................ 42

Peer System Policies & Practices Tables ...................................................................................................................... 47

IPEDS Variables: Definitions and Source Reference .................................................................................................... 53

Instructional Staff .................................................................................................................................................... 53

Non-Medical and Non-Instructional Staff ................................................................................................................ 53



About the Authors The Center for Economic and Business Research (CEBR) is an outreach center at Western Washington

University, located within the College of Business and Economics. The Center connects the resources

found throughout the University to assist for-profit, non-profit, government agencies, quasi-government

entities and tribal communities in gathering and analyzing useful data. We use a number of collaborative

approaches to help inform our clients so that they are better able to hold policy discussions and craft

decisions.

The Center employs students, staff and faculty from across the University as well as outside resources to

meet the individual needs of those we work with. Our work is based on academic approaches and rigor.

We not only provide a neutral perspective in our analysis, but also provide applied learning

opportunities. We focus on developing collaborative relationships with our clients and facilitating

meaningful discussions, instead of simply delivering an end product.

The approaches we utilize are insightful, useful, and are all a part of the debate surrounding the topics

we explore. However, none are absolutely fail-safe. Data, by nature, are challenged by how it is

collected and leveraged with other data sources; following only one approach without deviation is ill-

advised. We provide a variety of insights within our work – not only on the topic at hand but the

resources (data) that inform that topic.

We are always seeking opportunities to bring the strengths of Western Washington University to

fruition within our region. If you have a need for analysis work or comments on this report, we

encourage you to contact us at 360-650-3909. To learn more about CEBR visit us online at

https://cbe.wwu.edu/cebr/center-economic-and-business-research.

The Center for Economic and Business Research is directed by Hart Hodges, PhD and James McCafferty.

About this Study The 2018 supplemental operating budget (ESSB 6032) provided funds to study the Community and

Technical Colleges’ compensation policies. Specifically, we were asked to look at academic, classified,

and professional employee compensation data, source of funding, and the duties or categories for which

that compensation is paid; identify comparable market rate salaries; incorporate data from OFM

compensation studies from 2017-19; and provide analysis on whether a local labor market adjustment

formula should be implemented.

In 2007-08 and 2013-14, the Washington State Board for Community and Technical Colleges (SBCTC)

worked with a consultant, MGT of America Consulting, LLC (MGT), and a stakeholder task force, to

complete a comparative analysis of salaries. This study was needed based on increasing difficulty at

colleges to recruit and retain high caliber faculty and administrative staff.

In this report we update the data obtained in the previous 2007-08 and 2013-14 studies and apply,

where meaningful, the same, methodology and analysis methods used in the previous studies. We use a

different approach when adjusting compensation for cost of living, and note in the report when and why

we are using the different approach.

https://cbe.wwu.edu/cebr/center-economic-and-business-research



This report provides SBCTC with collected community and technical college faculty and administrator

salary data gathered using IPEDS (Integrated Postsecondary Education Data System), JobsEQ, C2ER

(Council for Community and Economic Research), and other sources. The report also provides analysis of

the collected data for the national and regional compensation data, administration and faculty salaries,

comparison of nominal and adjusted cost-of-living average salaries, comparisons with the data results

from the previous studies, hard to hire variables and index, and an economic impact analysis of salary

increases.

Peer Groups In order to evaluate salary levels for administrator and instructional staff within Washington State’s

community and technical colleges, we compared salary levels relative to other states. There are three

Peer Groups made up of various U.S. states. One of the groups is the Regional Peer States (RPS) which

consist of all of the Pacific Northwest States around Washington. Next is the Global Challenge States

(GCS) group which is a make-up of the top 10 states in the New Economic Index, produced by the

Information Technology & Innovation Foundation (ITIF) and used by Washington Learns. The ITIF uses 21

indicators that measure how well a state competes in the global economy.1 Lastly, we have the MGT-

Selected Peer States (MGT) group. The methodology for this last group was designed and used by MGT

during the 2007-08 Salary Study, which determines the top 15 states that compare to Washington based

on their state economic factors and colleges’ total revenue (i.e. Tuition & Fees; Federal, State, and Local

Appropriations), enrollment size, and number of faculty.

As updated through application of the previously developed methodology, the Peer Groups2 include:

1. Regional Peer States (RPS): California (CA), Oregon (OR), Idaho (ID), Montana (MT), and Nevada

(NV)

2. Global Challenge States (GCS)3: California (CA), Colorado (CO), Connecticut (CT), Maryland (MD),

New Jersey (NJ), New York (NY), Utah (UT), Virginia (VA), and Washington (WA).

3. MGT-Selected Peer States (MGT)4: Arizona (AZ), California (CA), Colorado (CO), Connecticut

(CT), Illinoi (IL), Kansas (KS), Massachusetts (MA), Maryland (MD), Main (MN), North Carolina

(NC), New Jersey (NJ), New Mexico (NM), Texas (TX), Virginia (VA), and Washington (WA).

In regards to the GCS group, we changed the sample size from seven states to ten states since ITIF’s

index involves the top ten states. Additionally, Delaware was listed as the fifth state in the New

Economic Index, however because IPEDS does not have any institution information on colleges in the

State of Delaware, we had to leave Delaware out and use the next available state on the list. Lastly, for

simplicity and continuity, we continued with MGT’s methodology for the MGT group so that

comparison(s) between the 2013-14 study and 2016-17 studies may be made.

1 The 2017 State New Economic Index, Information Technology and Innovation Foundation, (pp. 6-12), 2017. http://www2.itif.org/2017-state-new-economy-index.pdf 2 For a visual representation, see Map 1 at the end of this report. 3 The 2017 State New Economic Index, Information Technology and Innovation Foundation, (pp. 6-12), 2017. http://www2.itif.org/2017-state-new-economy-index.pdf 4 MGT of America Consulting LLC, Final Report Faculty and Administrator Salary Study Update, 2013-14. To our knowledge this report was not referenced in prior salary adjustments. https://www.mgtconsulting.com/

http://www2.itif.org/2017-state-new-economy-index.pdf?_ga=2.128797900.1120633729.1534173746-1009631850.1530223107

http://www2.itif.org/2017-state-new-economy-index.pdf?_ga=2.128797900.1120633729.1534173746-1009631850.1530223107

https://www.mgtconsulting.com/



Methodology This study entailed data collection and analysis of system’s policies and practices, as well as faculty and

administrator salaries. In conducting our research, we implemented several changes and adjustments

throughout this report to reflect either data revisions, methodology clarifications, or framework shifts

that were applicable since the 2013-14 study. Most changes were made in order to improve upon

previous methods, allowing for a more accurate analysis; such as the sample size of the peer groups, a

region-to-region focus on Cost of Living (COL) by Washington’s Workforce Development Areas, and the

exclusion of the “SBCTC Enrollment and Budget as Predictors of Average Salary” and “Local Funding as

Predictor of Nominal Average Faculty Salaries” regression models.

We chose to exclude both of the Average Salary regression models because until June 2018 colleges did

not have the authority to negotiate faculty salary levels. With such a recent policy change in local-

negotiation-of-salary, we saw no reason to include the regression models that predict the correlation

between average salary levels with enrollment, budget, and local funding. That being said, over the next

few years as colleges have more robust enrollments, larger budgets, and further exposure to local-

salary-negotiations we recommend reevaluating these regressions in the next salary report. This way it

will be possible to see if a correlation exists between average salary levels and the given predictor

variables. We explain other changes in the sections below.

Peer System Review of Policies & Practices In the 2007-08 and 2013-14 studies, eight state college systems participated in a survey providing

information on their system’s policy and practices for: Funding, Staff Compensation, Cost-of-Living

Salary Adjustments, Faculty Contract Days, Faculty Workload policies, Part-Time Faculty Compensation,

Collective Bargaining policies, Healthcare & Retirement benefits, and Other College-Specific benefits.

The survey information was collected by MGT through a direct survey sent out to all college systems in

the peer states. The college systems that participated are the following: Colorado Community College

System, New Mexico Independent Community Colleges, North Carolina Community Colleges, Texas

State Technical College System, Virginia Community College System, SUNY Community College System

(New York), Florida Department of Education, and Minnesota State Colleges and Universities.

This study updates the same college systems’ information by conducting primary research from each

systems’ policies and practices. The tables for this can be seen at the end of this document under the

section titled, Peer System Policies & Practices Tables. As in the past report, we have not used data

available from College and University Professional Association for Human Resources’ (CUPA-HR) survey,

due to low participation rates and data inconsistencies.



Cost of Living The following section describes the differences in the cost of living in different parts of Washington

State and applies a Cost of Living (COL) factor to faculty and administrator salaries in order to adjust for

the cost of living in a given region. This section is broken into two parts:

Part I – Workforce Development Area Average COL Weighted by Population

Part II – State Average COL Weighted by Population

Note: The methodology in this section differs from previous reports. In earlier reports, MGT looked at

how the cost of living in Washington compared to other states, but did not look at differences in the

cost of living in different parts of the state. We note that applying the same cost of living adjustment

factor to salaries in different parts of the state can be misleading.

By looking at COL averages through a micro- and macro-lens we are attempting to account for the

different living costs within Washington State and in Washington State compared to other states.

The source of the COL data comes from the C2ER, which produces a Cost of Living Index three times per

year, providing a measure of how the cost of living differs in a range of urban areas across the country.

The index is based on a basket of products that reflect different categories of consumer expenditures

(e.g. Grocery Items: steak, lettuce, etc.; Housing: apartment rent, home price, etc.; Utilities: electricity,

gas, etc.) The Cost of Living Index measures relative price levels for consumer goods and services in

participating areas, with participants collecting the same data (using the same list of items) in all

locations during a three-day period. The average for all participating places, equals 100, and each

participant’s index is read as a percentage of the average for all places.5 In this sense, the cost of living in

any given location can easily be seen as a certain percentage above or below the national average. In

addition, the index allows easy comparisons of the cost of living in different places at a given point in

time. (MGT also used the C2ER COL data in previous reports.)

(In disclosure, The Center collects and provides data for C2ER for 5 Washington State counties. This

makes us very familiar with the data collected and the methodology utilized.)

Part I Workforce Development Area Average COL Weighted by Population Washington State is organized in workforce development areas which reflect workforce areas that have

definable characteristics (e.g. labor areas, industry, geography, etc.). Each of these areas has stand-alone

boards that advise and direct workforce efforts which are traditionally very connected to the two-year

colleges within their boundaries.6 The workforce development areas were defined with the intention of

providing collaboration at the local-level in order to serve regional economies more effectively.

We view the 12 workforce areas in the state as regions that can have meaningful differences in cost of

living. In this section we discuss the different impacts on salary levels when adjusting by (1) a workforce

5 Council for Community and Economic Research (C2ER), Cost of Living Index, 2018 Q1. 6 For a visual representation, see Map 2 at the end of this report.



development area COL verses (2) an overall state average COL, both being weighted by population in

order to account for extreme regional variances.

In order for Washington State community and technical colleges to keep-pace with national salary

figures, it is necessary to consider peer states and their salary levels within community and technical

colleges. However, throughout this study we found that it would also be beneficial to consider

implementing regional salary competition within Washington State. By considering the COL index (COLI)

in each workforce development area (12 in total), we are able to better understand how pay differed

across the state, in both nominal terms and adjusted for cost of living. Without this regional comparison,

salaries in the Seattle-King and Snohomish workforce areas would be viewed as “better” if they were

just a little higher than salaries in the South Central and Benton-Franklin workforce areas because the

implicit assumption would be that the cost of living is the same everywhere in the state.7 Furthermore,

because an overall state COL average is necessary for national comparisons, we attempted to correct for

this statistical skew by weighting each counties COLI by population. The following example portrays the

potential salary differences between non-weighted- and weighted-state, and weighted-regional COL

adjusted salaries.

COL Index Values:

Simple, unweighted average of COLI values across the state: 111.5

Population weighted average COLI value for the state: 123.5

Example workforce area, population weighted average COLI values, which takes into consideration the

COL values from different counties in a workforce area and the population in the different counties

(detailed description and numbers provided below)

• Seattle King: 151.7

• Northwest: 112.8

• South Central: 94.0

Application:

If a faculty member is paid $59,977 on average at North Seattle College and we adjust this wage

using the unweighted state COLI average = 111.5 (not weighted by population), then their adjusted

pay (what it would “feel” like) is ($59,977/111.5)*100 = $53,791. This adjustment accounts for the

fact that a bundle of goods in Washington costs roughly 11.5% more than elsewhere in the U.S. Or,

put another way, paying someone at North Seattle College $59,977 per year is equivalent to paying

someone living in a city that has a cost of living the same as the national average a salary of $53,791.

If we adjust the wage with a population weighted state average COLI = 123.5, then the adjusted pay

is ($59,977/123.5)*100 = $48,564. The population weighted average is higher because of the larger

share of population living in the high cost areas of the Puget Sound. And the effective or adjusted

pay is lower because it reflects the higher cost of living.

If we use the workforce development area, population weighted average COLI for Seattle of 151.7,

then the adjusted pay is ($59,977/151.7)*100 = $39,537. The adjustment difference is much larger

7 We discuss the impacts of this purchasing power difference later in the Economic Impact Analysis section.



than both the weighted and non-weighted state average COLI adjustment because we are

considering the cost of living in that region rather than the state as a whole. In other words, their

purchasing power in Seattle is significantly reduced by how expensive basic living costs are in Seattle

versus another area in Washington State.

There is a significant cost of living expense not captured or accounted for when using both the non-

weighted and weighted state average COL. It is helpful to use the state average COL methods when

comparing how much it costs to live in Washington compared to anywhere else in the U.S. However,

it is not helpful to use this method when attempting to capture how much it costs to live in one area

of Washington compared to another area in Washington. The workforce development area COL

average provides a more accurate representation of these regional costs. We would like to note that

the state average COL method is not incorrect but rather inaccurate when calculating salaries for

different areas within Washington State relative to one another. We highlight here that this impacts

nearly all the areas of the state to varying degrees and reference Seattle only because of the obvious

magnitude.

Focusing in on the methodology, again, the workforce development area COL values were developed

using county-level COL indexes from JobsEQ by Chmura Economics & Analytics8 and county population

levels from the U.S. Census Bureau.9 We created weighted average COL indexes for each workforce

development area by summing the products of counties’ population times the COL within a workforce

development area and dividing by the sum of the counties’ populations within that same workforce

development area.10

𝑊𝑜𝑟𝑘𝑓𝑜𝑟𝑐𝑒 𝐷𝑖𝑠𝑡 𝐶𝑂𝐿 = ∑ [(County Pop1 ∗ COL1) + (County Pop2 ∗ COL2) + … + (County Popi ∗ COLi)]

∑ (County Pop1 + County Pop2 + … + County Popi)

Figure 1 (below) depicts the weighted averages of the COL by workforce development areas in

Washington State and shows which colleges fall under a given workforce area.11 Figure 2 depicts the

same values but also includes Washington State’s weighted average COLI (the yellow bar) for reference

purpose. This figure demonstrates how a single measure for cost of living would not result in meaningful

numbers when adjusting salaries for cost of living differences. The Seattle-King workforce area has the

highest COLI at 151.7. In other words, it costs 51.7% more to live in the Seattle-King workforce

8 JobsEQ by Chmura Economics & Analytics is a software service that provides users 24-hour access to economic development, education, and workforce market data for all of the United States. JobsEQ collects their COL data from the C2ER, 2018 Q1. 9 The U.S. Census Bureau only collects population data at the county level every 10 years because of frequent migration. The most recent 10 year mark occurred in 2010, thus the data that CEBR has collected are Census projections using 2010 numbers. 10 In constructing the data points for this series we only considered the COL for the county (or counties) in which colleges’ reside-in, in order to better project a COLI that would be experienced by employees. It is possible a person may choose to have a longer commute to reduce their cost of living, but in most cases the amount saved is minimal. 11 For a visual representation of these numbers, see Map 3 at the end of this report.



development area than the average in the United States. The South Central workforce area has the

lowest COLI in the state at 94.0, which means it cost 6.0% less to live in the South Central workforce

development area compared to the average in the United States.

[This space left intentionally blank]





Figure 3 shows the COL by county for the convenience of understanding the COL differences between

the counties in each workforce area. We do not recommend using the ‘by county COL’ as a

measurement over the ‘by workforce area COL,’ because it will not account for those that live in a given

county but work in a neighboring county. Workforce development areas account for a large amount of

this across-regions commute and economic development/behavior activities.



Part II State Average COL Weighted by Population In this section we show the population weighted average state COLI for all of the peer group states, so

that comparisons can be made between Washington State and its Peer Groups. Though this section is

short, we show all of the weighted average state index values for reporting and reference purposes.

These averages are used in the next section (Faculty Salary) when applied to the average faculty salary

levels.

A similar methodology to Part I was used in this section when calculating the population weighted

average state COLI: summing the product of all of the counties in the state and the counties’ population,

and then dividing by the sum of all of the counties’ total population. (We can think of this as an

aggregate of the workforce area method)

As mentioned before, the challenge with calculating an average COLI for the state stems from the fact

that people are not distributed evenly within the state. For example, the population in Yakima is smaller

than the population in Seattle, therefore we do not want to assign the same weight to each workforce

areas’ COL when calculating a state average COL value, because this would cause some areas to be over-

or under- represented. In order to better reflect the state average COL, we have taken into

consideration the population base within each workforce development area. This also accounts for large

variances in COL-levels throughout a given workforce development area, such as the Southwest

workforce development area which contains Lower Columbia College (119.5) and Clark College (126.5).

Figure 4 depicts the weighted average COLI values for all of the states in the U.S.12 When looking at the

results, Washington State stands five positions higher, at 10th place, when compared to the COL

adjustments from the 2013-14 salary study.13

[This space left intentionally blank]

12 It is critical to keep in mind that there will be significant variances by region within all of these states. 13 Conducted by, MGT of America Consulting LLC, Final Report Faculty and Administrator Salary Study Update, 2013-14. To our knowledge this report was not referenced in prior salary adjustments. https://www.mgtconsulting.com/

https://www.mgtconsulting.com/





Faculty Salary Similar to the prior section (Cost of Living), this section is broken into two parts, a micro- and macro-lens

that looks at average faculty salary levels:

Part I – SBCTC Faculty Salaries

Part II – Peer Group Salaries

All of the data for faculty salaries has been collected from the Integrated Postsecondary Education Data

System (IPEDS) which is the Department of Education’s National Center for Education Statistics’. IPEDS

collects the data through a self-reported survey by all postsecondary institutions. Because the

information is independently reported, it is important to note that there may be discrepancies within

the data compared to actual salaries. IPEDS also allows institutions to revise their information for up to a

year after the survey has been put into the public-online database. The updated information is not

reflected on the public-online database until the following year, thus giving another reason for

discrepancies within the data if you are looking at the data used in a report done one year and a similar

report using the same data, but completed a year or two later. This study relies upon 2016-17 salaries

for its comparisons – this is the most recent publication of IPEDS data on their public-online database.

The main IPEDS variables used in this portion of the analysis are, Salary Outlays for Instructional Staff

Equated to a 9-month Contract Total, and Instructional Staff - Total. These variables reflect a total salary

value for 9, 10, 11, and 12-month non-medical instructional staff at each community and technical

college in 2016-17, and the number of 9, 10, 11, and 12-month full-time non-medical instructional staff

that worked at each college in 2016-17. IPEDS does not report salaries for part-time non-medical

instructional staff, therefore we were unable to collect this data.

Part I SBCTC Faculty Salaries Figure 5 displays the average salary in 2016-17 for each of the 34 colleges under the Washington State

Board for Community and Technical Colleges (SBCTC). Since IPEDS has not updated their “Prior Year

Data Revisions” we obtained these variables from SBCTC’s data department. They have early-access to

the prior-year-revised data, causing their numbers to be a more accurate representation of the salary

numbers. Bellevue College holds the highest average total salary outlays for instructional staff, at

$65,989, and Olympic College and Walla Walla Community College tie for the median-level of average

total salary outlays for instructional staff, at $60,602 and $60,502 respectively.





Part II Peer Group Salaries The macro-lens focuses on the peer groups and states that make up those peer groups relative to

Washington’s average faculty salary level. This is where the COL indices from the Cost of Living section,

Part II, Figure 4 are applied. All state salary averages were calculated by the sum of each colleges’ total

salary outlays divided by each colleges’ total number of faculty.

The following figure 6 depicts the weighted average faculty salaries of all three of the peer groups,

Washington State (SBCTC), and the National average. There are two bars representing each group’s

salary averages. The light blue bar measures the nominally adjusted state weighted average faculty

salary:

𝑊𝐴 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑔 𝑆𝑎𝑙𝑎𝑟𝑦 =[(𝐹𝑎𝑐𝐶𝑜𝑙𝑙𝑒𝑔𝑒1 ∗ 𝐴𝑣𝑔 𝑆𝑎𝑙𝐶𝑜𝑙𝑙𝑒𝑔𝑒1) + (𝐹𝑎𝑐𝐶𝑜𝑙𝑙𝑒𝑔𝑒2 ∗ 𝐴𝑣𝑔 𝑆𝑎𝑙𝐶𝑜𝑙𝑙𝑒𝑔𝑒2) … ]

[𝐹𝑎𝑐𝐶𝑜𝑙𝑙𝑒𝑔𝑒1 + 𝐹𝑎𝑐𝐶𝑜𝑙𝑙𝑒𝑔𝑒2+. . . ]

and the dark blue bar measures the COL adjusted state weighted average faculty salary:

𝑊𝐴 𝐶𝑂𝐿 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑔 𝑆𝑎𝑙𝑎𝑟𝑦 = (𝑊𝐴 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑔 𝑆𝑎𝑙𝑎𝑟𝑦) ∗ (𝑊𝐴 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝐶𝑂𝐿 𝐹𝑎𝑐𝑡𝑜𝑟)

The “WA Adjusted COL Factor” variable was calculated by taking Washington State’s average COLI value

(123.5) and dividing it by each peer group state’s average COLI value. By doing this we are adjusting (or

normalizing) all of the COL values, and by association salary levels in other states relative to Washington

State.



The percentage changes between “WA SBCTC” weighted average salaries and the Peer Groups’

weighted average salaries are the following:

Table 1: 2016-17 Percentage Change of Faculty Salary from WA SBCTC to Peer Group

Nominally Adjusted COL Adjusted

RPG = -1.3% RPG = 7.7%

GCS = 7.7% GCS = 4.7%

MGT = 3.1% MGT = 12.4%

National = -5.0% National = 11.8%

WA SBCTC’s Nominal Weighted Average Faculty Salary level is 3.1% less than their MGT peers, and WA

SBCTC’s COL Weighted Average Faculty Salary level is 12.4% lower than their MGT-Selected peers. In

other words, Washington institutions currently pay, on average, 3.1% less than those states most similar

to Washington, and when COL adjustments are applied, Washington pays faculty 12.4% less than those

states it is most similar to. While WA SBCTC’s salary levels still fall behind their MGT peer states, the gap

between the two groups has decreased. Compared to the 2013-14 Salary Study,14 the gap between

Washington State’s COL adjusted weighted average faculty salary level and it’s MGT peers has

decreased 1.3% (from 13.7% to 12.4%).

Table 2: 2013-14 Percentage Change of Faculty Salary from WA SBCTC to Peer Group


RPG = 9.2% RPG = 9.0%

GCS = 18.1% GCS = 4.4%

MGT = 10.8% MGT = 13.7%

National = 2.1% National = 4.3%

A significantly deep question to ponder when looking at this data is who the CTCs are competing with

when attracting and retaining high quality instructional staff. High quality faculty members are price

sensitive, while also looking for institutions that reflect their values and interests. Some faculty can be

place-bound due to spouses or other externalities creating the wage competition not necessarily from

teaching institutions only but also from discipline related employers that are seeking the same skills. The

last section of this report, Hard to Hire, touches on this topic and possible solutions to consider.

14 No funding or salary adjustments resulted from the 2013-14 Salary Study conducted by MGT of America Consulting LLC.



Administrator Salary

This section examines eleven general administrator positions relative to SBCTC’s peer group states. All of

the data for administrator salaries has been collected from the Integrated Postsecondary Education Data

System (IPEDS) which is the Department of Education’s National Center for Education Statistics. As

mentioned above in the Faculty Salary section, IPEDS collects the data through a self-reported survey by

all postsecondary institutions, therefore the same discrepancies may apply. IPEDS defines all of the

variables used in this section as, non-medical and non-instructional postsecondary occupations.

Throughout this report we will refer to these occupations as “administrator positions,” though it has the

same meaning as the term “exempt staff” which is used throughout SBCTC’s system. The eleven

variables extracted from IPEDS database are the following : Librarians, Curators, Archivists, and

Academic Affairs and Other Education Services; Management Occupations; Business and Financial

Operations; Computer, Engineering, and Science Occupations; Community, Social Service, Legal, Arts,

Design, Entertainment, Sports and Media Occupations; Healthcare Practitioners and Technical

Occupations; Service Occupations; Sales and Related Occupations; Office and Administrative Support

Occupations; Natural Resources, Construction, and Maintenance Occupations; and Production,

Transportation, and Material Moving Occupations.15

The administrator salary averages were computed by taking the total number of salary outlays for a

given variable and dividing it by the number of people working in that given position. IPEDS defines

these variables from the Bureau of Labor Statistics – 2018 Standard Occupational Classification (SOC

codes).16

It is important to note that there may be some outliers that have caused drastic changes in the

percentage change results. We believe much of this is due to inaccurate reporting of institutions to

IPEDS because administrator categories and job descriptions are different for every system, and can

even be categorized differently throughout a system. Even with this discrepancy we believe IPEDS’s

variables to be the best representation and categorization for administrator positions because each

variable is the leading variable of a general category section in the SOC system, therefore various jobs

alike can fall under it as a subcategory (e.g. Under the Business and Financial Operations variable (SOC

13-0000) is a sub-variable such as general Human Resource Occupations (SOC 13-1070)). To insure that

this was the case, we went through administrator job titles and descriptions provided by SBCTC17 and

matched those titles to IPEDS SOC code variable names and descriptions and found that the SOC codes

are a sufficient representations of generalized administrator positions at community and technical

colleges.

15 IPEDS database provides a total of thirteen variables, two of which we chose to leave out because there was not enough data reported by each state for “Research Occupations” and “Public Service Occupations.” 16 Definitions and reference to all IPEDS variables can be found at the end of this report. 17 Washington State Board for Community and Technical Colleges, Excel File: 2018 Admin Salary Report, 2018.



Figure 7 depicts the weighted average salaries for Librarians, Curators, Archivists, and Academic Affairs

and Other Education Services of all three peer groups, Washington State (SBCTC), and the National

average. The calculations were conducted the same as those in figure 6.

The percentage differences between “WA SBCTC” weighted average salaries and the Peer Groups’

weighted average salaries for Librarians, Curators, Archivists, and Academic Affairs and Other Education

Services are the following:

Table 3: 2016-17 Percentage Change of Librarians, Curators, Archivists, and Academic Affairs and Other

Education Services Salary from WA SBCTC to Peer Group


RPG = -1.7% RPG = 7.0%

GCS = 18.6% GCS = 15.2%

MGT = 11.0% MGT = 20.9%


WA SBCTC’s Nominal Weighted Average Salary level for Librarians, Curators, Archivists, and Academic

Affairs and Other Education Services is 11.0% less than their MGT peers, and WA SBCTC’s COL Weighted

Average Salary level for Librarians, Curators, Archivists, and Academic Affairs and Other Education

Services is 20.9% lower than their MGT-Selected peers. This large difference could be due in part to

discrepancies in the data, such as some salaries having been updated in the system and others not

visible yet.



Figure 8 depicts the weighted average salaries for Management Occupations of all three peer groups,

Washington State (SBCTC), and the National average.


weighted average salaries for Management Occupations are the following:

Table 4: 2016-17 Percentage Change of Management Occ. Salary from WA SBCTC to Peer Group


RPG = 17.8% RPG = 28.0%

GCS = 24.8% GCS = 21.1%

MGT = 19.6% MGT = 30.4%




Figure 9 depicts the weighted average salaries for Business and Financial Operations of all three peer

groups, Washington State (SBCTC), and the National average.


weighted average salaries for Business and Financial Operations are the following:

Table 5: 2016-17 Percentage Change of Business and Financial Operations Salary from WA SBCTC to Peer

Group


RPG = 0.7% RPG = 10.0%

GCS = 18.7% GCS = 15.6%

MGT = 11.4% MGT = 21.6%




Figure 10 depicts the weighted average salaries for Computer, Engineering, and Science Occupations of

all three peer groups, Washington State (SBCTC), and the National average.


weighted average salaries Computer, Engineering, and Science Occupations are the following:

Table 6: 2016-17 Percentage Change of Computer, Engineering, and Science Occ. Salary from WA SBCTC

to Peer Group


RPG = -9.4% RPG = -1.4%

GCS = 0.9% GCS = -1.7%

MGT = -4.2% MGT = 4.2%




Figure 11 depicts the weighted average salaries for Community, Social Service, Legal, Arts, Design,

Entertainment, Sports and Media Occupations of all three peer groups, Washington State (SBCTC), and

the National average.


weighted average salaries for Community, Social Service, Legal, Arts, Design, Entertainment, Sports and

Media Occupations are the following:

Table 7: 2016-17 Percentage Change of Community, Social Service, Legal, Arts, Design, Entertainment,

Sports and Media Occ. Salary from WA SBCTC to Peer Group


RPG = 12.3% RPG = 22.9%

GCS = 14.3% GCS = 10.8%

MGT = 8.7% MGT = 18.4%




Figure 12 depicts the weighted average salaries for Healthcare Practitioners and Technical Occupations

of all three peer groups, Washington State (SBCTC), and the National average.


weighted average salaries for Healthcare Practitioners and Technical Occupations are the following:

Table 8: 2016-17 Percentage Change of Healthcare Practitioners and Technical Occ. Salary from WA

SBCTC to Peer Group


RPG = -4.4% RPG = 2.8%

GCS = 7.4% GCS = 5.5%

MGT = -0.2% MGT = 9.3%




Figure 13 depicts the weighted average salaries for Service Occupations of all three peer groups,

Washington State (SBCTC), and the National average.


weighted average salaries for Service Occupations are the following:

Table 9: 2016-17 Percentage Change of Service Occ. Salary from WA SBCTC to Peer Group


RPG = -4.0% RPG = 3.9%

GCS = 3.0% GCS = -0.4%

MGT = -0.4% MGT = 8.1%




Figure 14 depicts the weighted average salaries for Sales and Related Occupations of all three peer

groups, Washington State (SBCTC), and the National average.


weighted average salaries for Sales and Related Occupations are the following:

Table 10: 2016-17 Percentage Change of Sales and Related Occ.Salary from WA SBCTC to Peer Group


RPG = -5.8% RPG = -4.8%

GCS = 28.1% GCS = 24.1%

MGT = 17.9% MGT = 25.7%




Figure 15 depicts the weighted average salaries for Office and Administrative Support Occupations of all

three peer groups, Washington State (SBCTC), and the National average.


weighted average salaries for Office and Administrative Support Occupations are the following:

Table 11: 2016-17 Percentage Change of Office and Administrative Support Occ. Salary from WA SBCTC

to Peer Group


RPG = 1.7% RPG = 11.1%

GCS = 6.6% GCS = 3.4%

MGT = 0.3% MGT = 8.8%




Figure 16 depicts the weighted average salaries for Natural Resources, Construction, and Maintenance

Occupations of all three peer groups, Washington State (SBCTC), and the National average.


weighted average salaries for Natural Resources, Construction, and Maintenance Occupations are the

following:

Table 12: 2016-17 Percentage Change of Natural Resources, Construction, and Maintenance Occ. Salary

from WA SBCTC to Peer Group


RPG = 15.9% RPG = 26.6%

GCS = 26.1% GCS = 22.0%

MGT = 17.8% MGT = 28.0%




Figure 17 depicts the weighted average salaries for Production, Transportation, and Material Moving

Occupations of all three peer groups, Washington State (SBCTC), and the National average.


weighted average salaries for Production, Transportation, and Material Moving Occupations are the

following:

Table 13: 2016-17 Percentage Change of Production, Transportation, and Material Moving Occ. Salary

from WA SBCTC to Peer Group


RPG = 21.8% RPG = 24.6%

GCS = 19.1% GCS = 14.3%

MGT = 6.6% MGT = 15.8%




Economic Impact Analysis When adjusting salaries for cost of living, there are measurable impacts to the economy in the regions in

which the salary change takes place. These impacts are organized as direct, indirect, and induced effects

on the economy. Due to the nature of this sort of salary change – no increased demand for business

services and no change in business income – only induced effects, the spending completed by

employees of the company with their pay increase in their everyday lives, are relevant to consider.

These impacts are useful in understanding how a salary adjustment affects employment, output, and tax

revenue in a region, and can be used as part of a cost-benefit analysis.

To look at these impacts, we created a number of input-output models in IMPLAN, an impact analysis

software. These models – one for each workforce area in the state as well as a statewide model – each

contained data on two different salary change scenarios. Both were derived using the methodology

mentioned in the above section, Faculty Salary, and are used to illustrate the effects of different types

of salary change. We utilized two scenarios as there is no method to discern a market salary for faculty,

due to the fact that rates are not set by supply and demand. Therefore, we can illustrate what kind of

impacts we would expect to see under very different conditions. One scenario adjusts Washington

salaries to the purchasing power of the U.S. average salary for community and technical college faculty

while the other looks at a large increase in purchasing power for most around the state. The two

scenarios are as follows:

1. U.S. Average – all regions’ salaries are either adjusted to have the current purchasing power of

the U.S. average salary for faculty of community and technical colleges, based on data from

JobsEQ, or are left alone if already at or above the U.S. average. This is not necessarily the

market rate but does most closely reflect the reality of faculty pay at these institutions in the

United States.

2. South Central – all regions’ salaries are adjusted to have the current purchasing power of the

South Central workforce area, which has the lowest cost of living and no concurrent reduction in

salary, giving faculty there the largest average purchasing power amongst faculty in the state. In

essence we normalize all salaries to be the equivalent of those currently in the South Central

region while adjusting them for regional cost of living differences. This could be conducted using

any region as the base region, we chose South Central as it illustrated their relatively high

purchasing power.

Salary Adjustments by Scenario The U.S. scenario is perhaps the closest to approximating a market rate. Some districts, such as rural

workforce areas with low costs of living, were not modified due to the fact that they are already paid at

or above the U.S. average purchasing power and any scenario that would indicate a reduction in pay has

been eliminated. Urban areas often saw pay increases to account for the increased cost of living. The

average pay increase amongst workforce areas was $15,912 per full-time instructor. The Seattle-King

and Snohomish workforce areas called for the largest pay increases simply to adjust for cost of living,

with the Olympic district not far behind.



The total appropriation required by the legislature to fund this strategy would be $42.3 million per

year.18

The South Central scenario, with its lower cost of living and relatively higher salary, has the largest

purchasing power in the state. Adjusting that salary to the cost of living in other regions to make

purchasing power equal across the state results in large pay increases in every workforce area (except

South Central because it is used as the baseline for the scenario). The largest increase would be seen in

the Seattle-King district, with its extremely higher relative cost of living – faculty there would need, on

average, a $37,000 increase to achieve the same purchasing power as faculty currently experience in

South Central. Most increases would not be as substantial, averaging $11,602 for all workforce areas

outside of South Central and Seattle-King.

The total appropriation required by the legislature to fund this strategy would be $69.4 Million per year.

This amount does not include administrator staff, nor part-time faculty which would require additional

funds to include.

The data on these salary adjustments are presented below in tables 14 and 15.

Table 14: Current Faculty Population, Cost of Living, and Salary Outlays by Workforce Area

Workforce Area COL # of Faculty Salary Outlays Current Avg. Salary

South Central 94.0 129 $ 7,859,480.00 $60,926

Benton-Franklin 95.4 127 $ 7,343,079.00 $57,820

Spokane 97.4 340 $ 19,762,267.00 $58,124

Eastern 97.8 118 $ 7,139,264.00 $60,502

North Central 100.4 118 $ 7,201,071.00 $61,026

Pacific Mountain 101.3 232 $ 13,938,090.00 $60,078

Tacoma-Pierce 108.7 443 $ 28,106,830.00 $63,447

Northwest 112.8 241 $ 15,235,467.00 $63,218

Southwest 125.2 267 $ 16,501,297.00 $61,803

Olympic 133.4 161 $ 9,558,920.00 $59,372

Snohomish 144.1 252 $ 15,694,549.00 $62,280

Seattle-King 151.7 1139 $ 69,793,802.00 $61,276

18 The total appropriation value comes from the difference between the current 9, 10, 11, and 12 month faculty salary outlays that are being payed and the scenario adjusted, COL adjusted 9, 10, 11, and 12 month faculty salary outlays. This value does not include administrator staff, nor part-time faculty.



Table 15: Adjusted Wages for Each Scenario by Cost of Living and Workforce Area. U.S. Average Annual

Salary Adjusted South Central

Annual Salary $53,893 $64,815.11

Workforce Area Avg. Desired Wage Avg. Desired Wage

South Central $ N/A $ N/A

Benton-Franklin $ N/A $ 61,833.61

Spokane $ N/A $ 63,129.92

Eastern $ N/A $ 63,389.18

North Central $ N/A $ 65,102.25

Pacific Mountain $ N/A $ 65,657.70

Tacoma-Pierce $ N/A $ 70,454.02

Northwest $ 65,491.60 $ 73,087.10

Southwest $ 72,722.88 $ 81,157.04

Olympic $ 77,494.03 $ 86,481.54

Snohomish $ 83,692.22 $ 93,398.57

Seattle-King $ 88,106.24 $ 98,324.52

Note: Workforce areas labeled “N/A” are not adjusted as they are either the baseline for the South Central Scenario

or already above the average U.S. purchasing power and no pay cuts are being considered.

Scope of Impact In the model, a Labor Income Change activity was modeled, using an employee compensation event.

This employee compensation event was set to the level of the salary change, or the difference between

current total salary outlays and the cost-of-living-adjusted total salary outlays. Each model was ran

twice, once for each scenario, and results are presented below in tables 16 and 17.

All values in the employee column indicate the number of jobs supported by the increased spending –

the induced effect – of the faculty in their community due to their increased salary. The Labor Income

column is an aggregate of increased employee and proprietor compensation in the region, including

increased pay to workers and business owners amongst businesses the faculty frequent. Total Value

Added represents the new value generated in the region by the spending of additional income. Finally,

the Output generated is the summation of these benefits, described as increases in production rather

than income.



Table 16: Economic Impact of U.S. Average COL salary adjustment by workforce area with adjusted

wage19

Scenario 1 - U.S. Average

Workforce Area Employment Labor Income Total Value Added Output

Northwest 2.5 $ 92,275.34 $ 183,393.50 $ 322,149.72

Southwest 11.7 $ 469,928.73 $ 931,399.61 $ 1,528,938.82

Olympic 11.5 $ 404,235.97 $ 836,906.59 $ 1,465,246.43

Snohomish 18.4 $ 754,665.85 $ 1,490,775.57 $ 2,370,945.11

Seattle-King 99.1 $ 5,853,040.36 $ 10,486,163.39 $ 16,076,724.63

Total 143.1 $ 7,574,146.25 $ 13,928,638.66 $ 21,764,004.71

Table 17: Economic Impact of South Central COL salary adjustment by workforce area with adjusted

wage20

Scenario 2 - South Central

Workforce Area Employment Labor Income Total Value Added Output

Benton-Franklin 2.3 $ 99,187.49 $ 184,040.55 $ 310,146.11

Spokane 11.1 $ 474,964.21 $ 860,726.77 $ 1,488,336.04

Eastern 1.3 $ 42,339.65 $ 89,619.16 $ 161,170.48

North Central 2.0 $ 73,051.85 $ 145,376.84 $ 254,768.38

Pacific Mountain 5.7 $ 224,625.34 $ 442,817.06 $ 753,498.14

Tacoma-Pierce 13.1 $ 620,075.25 $ 1,133,468.97 $ 1,825,342.22

Northwest 10.9 $ 400,503.53 $ 795,984.59 $ 1,398,229.53

Southwest 20.7 $ 832,873.77 $ 1,650,757.35 $ 2,709,800.35

Olympic 17.2 $ 604,716.09 $ 1,251,968.94 $ 2,191,932.81

Snohomish 26.7 $ 1,096,762.09 $ 2,166,556.40 $ 3,445,714.05

Seattle-King 136.8 $ 8,082,197.92 $ 14,479,867.33 $ 22,199,619.74

Total 247.8 $ 12,551,297.20 $ 23,201,183.96 $ 36,738,557.85

Scenario 2 generates larger induced effects in all categories than Scenario 1. This is not surprising, as

more workforce areas would realize a salary increase under Scenario 2 and those increases would be

larger. In both scenarios, the Seattle-King workforce area experiences the largest impacts, mainly due to

the extremely high cost of living in the area causing significant faculty wage adjustments.

One thing that is important to note is that oftentimes additional spending in a region helps create

employment and economic growth outside of the region or that faculty may be commuting from a

different workforce area, earning their income in one but spending it in another. These spillover effects

can be modeled by running the same scenarios in a Washington state model, to see how much of the

effect of a salary increase in one workforce district affects others. This analysis does not include spillover

19 For a visual representation of these numbers, see Map 4 at the end of this report. Titled: Scenario 1 Induced Effect on Employment by Workforce Development Area 20 For a visual representation of these numbers, see Map 5 at the end of this report. Titled: Scenario 2 Induced Effect on Employment by Workforce Development Area



effects into other surrounding areas, such as Oregon, Idaho, British Columbia, or elsewhere, although

these are expected to be relatively minimal. The results of the spillover models are presented below in

table 18. The numbers in the row labeled “State Model Effects” indicate the estimate for the entire

amount of economic impacts for Washington State for that scenario.

Table 18: Estimation of Spillover Induced Effects in Washington State

Economic Impact Spillover Estimation

Scenario 1 - U.S. Average

Employment Labor Income Total Value Added Output

Sum of County Effects 143.1 $ 7,574,146.25 $ 13,928,638.66 $ 21,764,004.71

State Model Effects 216.2 $ 11,111,090.19 $ 20,477,116.86 $ 34,098,932.02

Total Spillover 73.1 $ 3,536,943.95 $ 6,548,478.20 $ 12,334,927.31

Scenario 2 - South Central

Sum of County Effects 247.8 $ 12,551,297.20 $ 23,201,183.96 $ 36,738,557.85

State Model Effects 354.3 $ 18,209,293.36 $ 33,558,707.70 $ 55,882,676.27

Total Spillover 106.5 $ 5,657,996.16 $ 10,357,523.73 $ 19,144,118.42

Effects on employment, output, etc. are not the only changes to consider when looking at the impact of

a salary change around the state. The ripple effects of the initial salary boost also generate federal,

state, and local tax revenue. Those changes are enumerated in table 19 below.

Table 19: Estimation of Induced Effects on Tax Revenue in Washington State, Including Spillover Effects

Total Tax Revenue Impacts

Scenario 1 - U.S. Salary Average Scenario 2 - South Central

State and Local Taxes $ 1,488,553 $ 2,576,808

Federal Taxes $ 1,948,761 $ 3,270,831

Total S&L Spillover $ 777,344 $ 1,136,634

Total Fed Spillover $ 1,026,178 $ 1,604,615

Total S&L $ 2,265,897 $ 3,713,442

Total Fed $ 2,974,939 $ 4,875,446

Again, spillover effects are those that are not generated in the workforce area where a specific salary

change took place but are still captured in Washington State. Therefore, all state and local taxes in the

table above would go towards governmental bodies in Washington State for each scenario. It is also

important to remember that this is not a cost-benefit analysis, but rather an estimation of the effects on

tax revenue of a salary increase to adjust for cost of living.

The South Central scenario generates more tax revenue, mainly due to the fact that salaries are raised in

all workforce areas and by more than seen under the U.S. Average scenario. The tax revenue generated

in the U.S. Average scenario is roughly 60 percent of that seen in the South Central scenario.



Hard to Hire Factor Cost of living is only one salary adjustment necessary to account for regional differences in economic

conditions. The desirability of a location, the availability of work for a spouse, and other factors also may

play into whether a potential faculty or staff member at a technical or community college finds a given

salary to be acceptable. A location with less general amenities may struggle to maintain faculty and staff

if there is not a significant salary incentive. Vice versa, a location with more amenities in general will

often be more attractive to job applicants and therefore the salary incentive may not need to be as large

or may be negative if amenities are so valuable that people would rather take a relatively lower pay than

not live nearby.

In this section, we create “Hard to Hire Factors” (HTH) in an effort to account for this desirability

element to the job search. These factors are used to further adjust the COL adjusted salaries. The result

will be a salary value, in current dollars, that has taken into account regional amenities and cost of living.

While we know this model does not consider every possible factor in a person’s decision for accepting,

or not accepting a position it is meant to be a starting place to fuel a discussion surrounding salary

ranges used to attract and retain employees – specifically those difficult to fill.

Part of our task for this analysis was to create a quantitative measure of how salaries might need to be

adjusted to account for the different HTH factors. We have made an effort to identify likely factors and

show how salaries might need to be adjusted as a result of those factors. We also believe the various

technical and community colleges should interview employees and, if possible, employees who have left

and job candidates who did not accept an offer to learn why different positions may be hard to fill. Each

college likely faces HTH factors not included in the list provided in this analysis. Again, this section is

intended as a starting point for considering HTH factors and how salaries might need to be adjusted due

to those factors.



Methodology Six attributes were chosen to develop HTH factors for each Workforce Development Area. Since each

applicant is different and may value different regional amenities, attributes were chosen for their

relatively universal appeal. For example, climate is hard to quantify in terms of desirable or non-

desirable, as some prefer a warm climate while others prefer cold temperatures. The six attributes used

in our analysis are:

• Spousal Employment Opportunities

• Primary Education Student-to-Teacher Ratio

• Distance to Hospitals

• Retail Diversity

• Poverty Rate

• Crime Rate

Spousal Employment Opportunities Moving to a new region for a job can be much easier if your spouse can also find work in the new region.

To account for the fact that a spouse can work in a variety of industries, we collected total industry labor

demand data from JobsEQ by Chmura Economics and Analytics.21 This value was created by multiplying

the average annual wage for a job in an industry by its new, speculative demand (based on transfers,

exits, and industry growth). The resulting total for each industry in a Workforce Area was aggregated to

a total for the entire region. The total for each Workforce Area was divided by the population of the

Workforce Area to determine the total amount of dollars available per-capita in a Workforce Area.

Putting it in per-capita terms helped to control for extreme population differences between Workforce

Areas.

In more plain language, our approach follows this logic: if a region has a larger number of dollars

available per-capita, it is more likely that a spouse would find well-paying employment in that region. In

this way, we can compare the spousal employment prospects between Workforce Areas. An example of

this process is included below for the Olympic Workforce Area.

21 Occupational Snapshot, [YEAR]



Table 20: Total Industry Labor Demand for the Olympic Workforce Development Area

Industry Olympic

Agriculture, Forestry, Fishing and Hunting $5,681,868

Mining, Quarrying, and Oil and Gas Extraction $339,955

Utilities $4,005,568

Construction $35,590,902

Manufacturing $134,516,352

Wholesale Trade $11,119,090

Retail Trade $69,357,444

Transportation and Warehousing $14,326,620

Information $6,111,084

Finance and Insurance $13,093,960

Real Estate and Rental and Leasing $9,668,988

Professional, Scientific, and Technical Services $50,279,728

Management of Companies and Enterprises $3,632,496

Administrative and Support and Waste Management and Remediation Services

$19,247,052

Educational Services $49,772,483

Health Care and Social Assistance $99,052,518

Arts, Entertainment, and Recreation $17,307,150

Accommodation and Food Services $39,168,048

Other Services (except Public Administration) $17,957,275

Public Administration $65,237,640

Total - All Industries $704,879,593

Population 375,122

Total Dollars Available per Capita $1,879.07

Student-to-Teacher Ratio If a faculty or staff member is considering a move to a different region, they may have children to

consider as well. Low student-to-teacher ratios allow for greater personal interaction between the

student and teacher, and this factor can be used as a potential proxy for the quality and capacity of local

primary education. To measure this, we collected data on each district from the National Center for

Education Statistics – Institute of Education Sciences (IES), and in each Workforce Area were aggregated

to get an average ratio for each region. This allowed comparison between regions.



Distance to Medical Facilities Distance from the nearest hospital or medical facility might be a matter of comfort, but could be very

important. Therefore, we collected data on the average distance between a college and the nearest

hospital in its workforce region. Google Maps was used to determine distances and the results from

each college were averaged to get a distance value for each Workforce Area. At this point, the values

were comparable between Workforce Areas.

Retail Diversity There are a variety of physical amenities available on a regional basis, but retail is one that could matter

to everyone as shopping is required on a regular basis (whether for food, work clothes, or other items .

Measuring the strength of retail presence in a region is difficult. Our approach focuses on the

availability of a diverse retail market which serves as a proxy for the potential diversity of selection

available to consumers. This can be thought of as the amount of different types of retail in the area,

which would increase the likelihood of finding specific goods or services. Utilizing data from ESRI22, we

obtained information on shopping centers across the state of a certain minimum size: 100,000+ Gross

Leasable Area (sq ft) as of February 2015. Those centers were sorted into categories based on size,

including very small, small, medium, large, and very large. The size cutoffs for those categories are listed

in the table below:

Table 21: Retail Categorization Scale

Category Gross Leasable Area (sq. ft.)

Very Small < 200,000

Small 200,001 - 300,000

Medium 300,001 - 500,000

Large 500,001 - 800,000

Very Large 800,000 +

The mix of retail for each Workforce Area was then determined by dividing the number of a certain size

of shopping center by the total number of shopping centers in a Workforce Area. This created a

percentage value for each size of shopping center, for each Workforce Area. We could then compare the

mix of retail sizes across Workforce Area to determine which had a more appealing mix. The core

assumption made during this process is that if a Workforce Area had a greater percentage of one type of

retail than the state average, this was considered positive and appealing.

22 ESRI, Major Shopping Center Map, February 2015.



An example of the retail data for one workforce area is included below for illustrative purposes.

Table 22: Retail Categorization Scale and Results for the Olympic Workforce Development Area

Retail Size ->

Very Large Large Medium Small Very Small

Workforce Area

Number of Centers

% of Total

Number of Centers

% of Total

Number of Centers

% of Total

Number of Centers

% of Total

Number of Centers

% of Total

Total

Olympic 1 33% 0 0% 1 33% 1 33% 0 0% 3

Poverty Rate The poverty rate at all ages of every county in Washington was gathered from the Census Bureau23 and

multiplied by the same county’s population to get an overall level of impoverished people in the county.

Those levels were aggregated by Workforce Area and then divided by population to put the number

back into the form of a per-capita rate that can be used to build an index.

Crime Rate Crime data was gathered by county from the Washington State Statistical Analysis Center, under the

State Office of Financial Management24 and aggregated into Workforce Area. The data is in the form of

number of reported offenses at all levels of crime. Then, it was divided by population – similar to the

poverty level – to determine the amount of crime per-capita. This number was then compared across

Workforce Areas.

23 Small Area Income and Poverty Estimates, 2016 24 Criminal Justice Data Book, 2016



Results All of these factors, after being sorted into comparable numbers between Workforce Areas, were built

into indexes and then aggregated to find a single HTH factor for each region. All factors are weighted

equally. As with COL data an index score below 100 means it would easier to attract talent whereas

scores above 100 indicate the relative difficulty that attracting talent may incur.

Table 23: Hard-to-Hire Index

Workforce Area Spousal Employment

Student-to-Teacher Ratio

Distance to Hospitals

Retail Diversity

Poverty Crime HTH Index

Olympic 147.5 99.2 56.8 108.5 100.0 88.2 100.03

Pacific Mountain 143.1 102.8 83.0 90.5 111.0 93.3 103.95

Northwest 140.7 102.7 116.5 96.4 113.3 100.0 111.63

Snohomish 124.9 119.4 63.4 99.9 70.7 96.7 95.83

Seattle - King 14.3 113.3 116.2 101.2 82.2 106.9 89.00

Tacoma - Pierce 137.7 116.9 81.3 108.7 106.9 135.5 114.50

Southwest 136.9 109.2 28.4 87.9 91.5 79.7 88.94

North Central 136.0 92.8 172.6 120.6 133.5 92.4 124.64

South Central 139.8 99.7 48.1 84.3 157.8 113.9 107.24

Eastern 150.5 83.6 174.8 126.4 152.2 78.6 127.67

Benton - Franklin 118.9 93.1 104.9 115.5 105.1 89.9 104.57

Spokane 130.2 105.6 105.6 105.2 117.5 63.0 104.54

These HTH factors were then used to adjust salaries in the same fashion as the Cost of Living index

values. The adjusted salaries are presented below, based off of the Scenario 1 (U.S. Avg. Salary) results

in the Economic Impact Analysis section of this report. In essence the current salary is adjusted by the

relative COL value and further adjusted based on the HTH factor. These two independent variables may

either add or subtract to the resulting adjustment.



Table 24: Hard-to-Hire Factors Applied to COL Adjusted Average Faculty Salaries

Workforce Area Current Salary

COL Adj. Salary

HTH & COL Adj. Salary

Difference (HTH & COL vs Current)

Olympic $59,372 $77,494 $77,515 $18,142

Pacific Mountain $60,078 $58,834 $61,161 $1,083

Northwest $63,218 $65,492 $73,107 $9,889

Snohomish $62,280 $83,692 $80,202 $17,922

Seattle - King $61,276 $88,106 $78,413 $17,137

Tacoma - Pierce $63,447 $63,132 $72,289 $8,843

Southwest $61,803 $72,723 $64,682 $2,879

North Central $61,026 $58,337 $72,713 $11,687

Eastern $60,502 $56,802 $72,519 $12,016

Benton - Franklin $57,820 $55,408 $57,941 $122

Spokane $58,124 $56,569 $59,139 $1,015

Note: South Central is not presented in this table as the combination of COL and HTH adjustment would

result in a lower average salary for the region than currently offered and this report is not considering

the impacts of any pay reduction.

The Olympic Workforce Area would receive the largest pay increase per the results of our HTH and COL

factor adjustment, at $18,142. Seattle - King and Snohomish are not far behind at $17,137 and $17,922,

respectively. The smallest increase would be seen in the Benton-Franklin Workforce Area, at $122.

It is important to note that the HTH index we have developed do not and cannot account for all

variables that go into an applicant’s desire to move to a certain region. A more accurate but time-

intensive approach would be to develop HTH factors for specific cohorts – sorted by age, area of

expertise, or some other identifying category. However, in terms of components that are universally

appealing, our HTH index uses reasonable assumptions to rate the relative desirability of the different

Workforce Areas in Washington State.

A key component not included in this analysis is the substitute work value. An employee is employable

by other entities at a salary that may or may not be comparable to those presented in this work. While

this is an intriguing comparison point it is unrealistic to attempt the construction of a matrix to analyze

the possibilities due to the enormous number of variables possible.

One further consideration is that salary incentives may not be the most efficient or effective way to fill

roles at certain colleges around the state. Rotating staff through multiple colleges for short, intensive

classes or remote presentation classes where the instructor teaches via streaming video are some

options to be considered for schools in the hardest areas to hire.



Geographical Information System (GIS) Maps Map 1



Map 2



Map 3



Map 4



Map 5



Peer System Policies & Practices Tables Table P.P.1: Funding Policies & Practices

Table P.P.2: Compensation Practices

System Limitations on Setting Tuition Funding Sources Academic Year

Washington State Board for Community and Technical

Colleges

The state legislature sets the maximum amount

that tuition can be increased by the State Board.

State General Fund & Special Revenue 47%, Grants &

Contracts 19%, Local Dedicated Fund 10%, Tuition (aka:

Operating Fees) 24%

Quarter

Colorado Community College System

"All tuition rates, fees, and charges for services

that are specifically delineated below, must be

approved annually by the Board." -State board

Tuition, state funds, grants, contracts Semester

New Mexico Independent Community CollegesColleges have the ability to set the price of tuition

and fees

Gift donors, tuition and fees, state appropriations, local

mild levy appropriations, sources meant to support I&G

expendatures (general opperating costs)

Semester

North Carolina Community College SystemLocal colleges have the authority to determine

tuition pricesTuition, state funds, local funds, donors Semester

Texas State Technical College SystemTuition and fees must be approved by the board

of regentsTuition and fees, grants, general revenue, gifts, Semester

Virginia Community College System Statewide tuition rates are set by the state boardGrants, tuition, state funds, other revenues, gifts,

contracts Semester

SUNY Community College System Determined at the state level Tuition, government funds Semester

Florida Department of Education Decided by the school districts State appropriations, tuition and fees Semester

Minnesota State Colleges and UniversitiesColleges must charge tuition that adheres to

Minnesota statutes and policiesTuition and fees, state funds, grants, revenue fund Semester

2016-17 Results

Salary Schedules Determined Locally Merit Step

Washington State Board for Community and

Technical Colleges

Faculty salary schedules are locally negotiated by each

district. Administrators/exempt employees salaries are

locally set, primarily based upon a combination internal

alignment and market influences.

Faculty and adminstrator

salaries are set locally. Until

recently (June 2018), salary

increases could only occur if

authorized by the Legislature.

No No

Many colleges have negotiated

step increases for faculty.

Administrators and exempt

employees typically do not have a

salary schedule, no step increases.


Colleges have the authority to determine salaries: "The

board indents that the college presidents shall have sole

responsibility for establishing salary plans for faculty of

their colleges." -CCCS

Yes, but the salary pool for

each college is determined at

the state level

Pay increases are merit based, faculty that earns a

performance rating of "commendable" (the grades

are: exemplary, commendable, needs

improvement) or above are eligible for a pay

increase.

*The colleges decide the criterion for these

performance ratings.

"[P]lans must include a merit-driven process to guide individual

salary decisions. The salary allocation plans shall be reviewed by

the system president…there shall be two categories of salary

adjustments, base building and non-base building. Faculty may be

eligible for either of these types of salary adjustment. Merit shall

be the prevailing factor in all salary adjustments." -CCCS

New hires are given a starting

salary. Pay increases are largely

determined by merit, however

salaries increase for faculty that

has completed four semesters and

18 credits of teaching experience.

New Mexico Independent Community Colleges Faculty is placed on a salary range decided by the state.

Not determined locally. The

state board decides salaries

and salary increases.

Employees move up and down the salary range

based on performance.

The regular staff pay structure consists of 26 pay grades (A-Z). The

pay grades reflect comparative levels of knowledge, skills, abilities

and responsibilities.

Yes

North Carolina Community College System Determined locally but approved by the state board. Determined Locally

Yes, employees will have merit based increases in

salaries, however salaries cannot exceed the

maximum salary for their position.

Advancement from minimum to maximum salary is based on

merit.Yes

Texas State Technical College SystemSalary increases are part of the annual budget

formulation and job performance reviews.Determined Locally Yes

Yes - An increase in salary of at least $30 per month on Salary

Schedule A or an increase in salary on Salary Schedule B within the

same salary group.

No

Virginia Community College System Salary ranges are system-wide No Yes Salary increases are based on merit No

SUNY Community College System Minimum and maximum salaries for all colleges Determined with collective

bargaining (state level)Yes Yes Yes

Florida Department of Education XDetermined at the district

level

Yes, employees are evaluated.

*Highly effective employees see salary increase.Yes

Yes: "Salary adjustment for salary

schedule step." -FDE

Minnesota State Colleges and UniversitiesSalaries are increased at the end of the fiscal year, the

college’s president has the authority to increase salaries.Yes Yes, award for good performance X Yes

Pay for PerformanceIncreases

2016-17 Results

SystemCompensation Structure



Table P.P.3: COL Salary Adjustments

Table P.P.4: Faculty Contract Days

System Timeframe Across the Board Legislatively Mandated


Colleges

Received annual, but decided biannually. By

statute, Faculty receive an annual COL

adjustment based upon the King County CPI,

unless set aside by the Legislature.

Funds are allocated to colleges on an "across the board"

basis, but may be distributed to employees

differentially, based on local decision-making and

bargaining.

Yes

Colorado Community College SystemDepends on the annual budged, pay increases are

determined by merit.No No

New Mexico Independent Community CollegesAnnually, as granted by the board (depends on

the availability of funds)Yes No

North Carolina Community College System

Salaries can be adjusted annually or when a

faculty member is promoted, salary range is

determined by the state board.

Varies Yes

Texas State Technical College System Annually Varies No

Virginia Community College SystemSalary increases are based only on merit which is

based on performance evaluations.No No

SUNY Community College System Annually Yes No

Florida Department of Education Annually Varies No

Minnesota State Colleges and Universities Salaries are increased at the end of the year. X No

2016-17 Results

System Number How Determined

Washington State Board for Community and Technical Colleges Average: 177 (min: 166, max: 221) Local collective bargaining

Colorado Community College System 166 X

New Mexico Independent Community Colleges

Either Year Long (365 days) or Semester Long (110-111

days)

*Temporary employee contracts do not last longer than 6

months

Determined at the college level

North Carolina Community College System 9 Month Contracts

Contract length is for all state-colleges, however

individual colleges can choose to extend the

contract lengths.

Texas State Technical College System Employment contracts can last not longer than one year. System-Wide

Virginia Community College System 9 Month Contracts Determined at college level

SUNY Community College System 10 or 12 Month Contracts Determined at the state level

Florida Department of Education

210 days for temporary employees

9, 9.5, or 12 Month Contracts for Full-time Employees Numbers are standard for the whole state

Minnesota State Colleges and Universities X Renewed Annually

2016-17 Results



Table P.P.5: Faculty Workload Policies

System System-Wide Policy

Washington State Board for Community and Technical Colleges

Faculty workload varies based upon mode of instruction. Faculty are typically required to teach 15

credits or 165 contract hours per quarter in lecture/lab. Faculty are also required to have office hours,

advise students, and participate in college governance activities.


Each faculty member and instructor should not exceed 21 credit hours of work per semester. Employees

will be credited with 2.25 hours of service per week, and will be credited with an additional credit hour

for each additional hour of work per week.

New Mexico Independent Community Colleges

Nine month faculty teach 30 hours divided over fall, winter, and spring quarter and maintain no less

than 7 office hours, provide no less than 50 hours of college service per academic semester.

Twelve month employees should teach 38 hours every academic year with 15 pay hours taught during

the fall and spring, 7 office hours, required to work 40 hours per week.


Faculty adheres to their assigned 30 hour work week with at least 1 office hour per day and 5 office

hours per week, but may work as many as 40 every week.

Specifically for lectures/lab classes faculty is required 21 contact hours, for shop/lecture/lab classes (ex:

autobody repair) faculty are required 24 contact hours.

Texas State Technical College System Full time employees work 40 hours a week, part time employees work 20 hours a week.

Virginia Community College SystemAcademic-year workload is 33-39 credits with 39-49 contact hours. No more than 3 credit hours of

overload may be assigned in an academic year.

SUNY Community College System Varies depending on college - locally determined

Florida Department of Education Varies depending on college - locally determined

Minnesota State Colleges and Universities Faculty work 4o hour per weeks

2016-17 Results



Table P.P.6: Part-time Faculty Compensation

System How DeterminedVariation from Full-Time Compensation

Policies


Salaries are determined locally through collective

bargaining. Typically, part-time instructors are paid a flat

per credit rate, however, some colleges have negotiated

salary steps and pay for additional assigned duties.

Salary is based upon a negotiated per credit or

contact hour rate for work necessary to produce

the assigned credits for students (preparation,

teaching, evaluation). Additional pay for work

outside of teaching (advising, office hours, etc.)

may be incorporated into the per credit rate or

paid as a stipend.

Colorado Community College System All salaries are determined by the college's president X

New Mexico Independent Community CollegesPart time salary is calculated using the "pro-rata" hourly

rate of the minimum salary for each education level.

Pay is based on workload of any given course;

part time course compensation is calculated on

the workload times the appropriate hourly rate.


The pro-rata hourly rate of the minimum salary for each

education level shall be used to determine the minimum

salary for part-time faculty members.

The pro-rata hourly rate of the minimum salary

for adjunct faculty is calculated by dividing the

full-time faculty minimum by 1560 hours (number

of hours for 9/12th of a work year).

Texas State Technical College System Determined at college level

Part time salary is a percentage of the full time

salary. Part-time employees are normally placed

on the salary schedule at the minimum salary of

the grade for the position which they hold.

Virginia Community College SystemCompensation is pro-rated and benefits are restricted

consistent with state policies and regulations.

Part-time nine-month teaching faculty are

employed on a continuing basis to teach

approximately 60%-80% of a regular faculty

workload and carry regular faculty duties and

responsibilities.

SUNY Community College System

The salary ranges for full time employees (that the part

time salaries are based on) are determined by collective

bargaining. Prorating full time salaries is a state policy.

If a faculty member does not have a full load their

salary is prorated

Florida Department of Education X X

Minnesota State Colleges and Universities Determined by employer in contract Determined by employer in contract

2016-17 Results



Table P.P.7: Collective Bargaining Policies & Practices

Table P.P.8: Healthcare Benefits

System Institutions with Unionized Faculty Contract Terms Negotiated Affiliations


Colleges100% Most Terms

NEA - National Education Association

AFT - American Federation of Teachers

Colorado Community College System None None None

New Mexico Independent Community Colleges Some Most Terms New Mexico Employees Union

North Carolina Community College System None None None

Texas State Technical College System Some Some Texas Community College Teacher's Association

Virginia Community College System Some Some Virginia Community Colleges Association

SUNY Community College System 100% Negotiate Pay Increases

State University Professional Services Negotiating Unit,

Administrative Services Unit, Operational Services Unit,

Institutional Services Unit, Professional Agency Police

Services Unit, and Scientific and Technical Services Unit

Florida Department of Education 100% Most Terms Florida Education Association, United Faculty of Florida

Minnesota State Colleges and Universities, includes

technical colleges100% All Terms

Inner Faculty Organization, Minnesota State College

Faculty, Minnesota State University Association of

Administrative and Service Faculty, American Federation

of State, County, and Municipal Employees, Minnesota

Association of Professional Employees, Minnesota

Government Engineer Council, Middle Management

Association, and Minnesota Nurses Association

2016-17 Results

System % of Individual Salary Employee Cost Employer Portion


CollegesX

Depends on the insurance plan; for a single employee

the cost is between $45 and $162; for a full family the

price range is $79-$387

Health insurance is provided by the state

Colorado Community College System X XEmployer contributes $60/month ($720/year) to

employee's health savings account

New Mexico Independent Community Colleges X Varies 80, 75, or 65% of total premium

North Carolina Community College System Depends on the college's insurance plan

Depends on the insurance plan

Per person per year costs are between: $4,500 and

$6,500 (not including the employer-pad portion)

The maximum annual employer contribution rate (2016)

was $5,471 and $4,650 for Medicare employees.

However, the contribution rate varies for the 2017-18.

Texas State Technical College System Varies None 100%

Virginia Community College System Total premium depends on plan and family size Depends on the plan (Highest was around 15%) A portion of the insurance is paid for by the state

SUNY Community College System X 16% 84%

Florida Department of Education Determined Locally Determined Locally Determined Locally

Minnesota State Colleges and Universities No more than 60% of salary Depends on the plan: 0%, 50%, 75%, 100% of premiumDepends on the plan: 0%, 50%, 75%, 100% of premium.

The state also contributes to insurance premiums.

2016-17 Results



Table P.P.9: Retirement Benefits Policies and Practices

Table P.P.10: Other Benefit Policies and Practices

System % of Individual Salary Employee Contribution Employer Contribution

Washington State Board for Community and

Technical Colleges 10%, 15%, 20% (depending on age) 5%, 7.5%, 10% (depending on age) 5%, 7.5%, 10% (depending on age)


Retirement program called PERA

requires a payment of 8% of the

individuals salary every month X 50% match on employee contributions

New Mexico Independent Community Colleges 24.60% 10.70% 13.90%

North Carolina Community College System 16.12% Yes 17.13%

Texas State Technical College System 7.50% 3.75% 3.75%

Virginia Community College System X 5%, 7.5% or 10% (depending on age)

Yes, employer must match 100% of

employee's contribution

SUNY Community College System Various ranges from 3% - 6% Various ranges from 3% - 6%

8% contribution for the first 7 years,

and then 10% contribution thereafter

Florida Department of Education 6.50% 3% 7%

Minnesota State Colleges and Universities X 4.5%, 5.5% or 7.5% 5.5%, 6%, 7.5%

2016-17 Results

System Detail


Life Insurance, Dental Insurance, Vision, Flexible Spending Arrangements (FSA), Dependent Care

Assistance Program (DCAP), Health Savings Account (HSA), Auto and Home Insurance, Health Savings

Account, Long-term Disability Benefits, Voluntary Employee Benefits Account, and Long-term

Care/Assistance Services


Tuition Assistance, Dental Insurance, Vision Insurance, Flexible Spending Accounts, Life Insurance,

Supplemental Retirement Plans, PERA Information, Disability Benefits, Worker's Compensation Benefits,

Faculty Sick Leave, Funeral Leave, Holiday Leave, Jury-Duty Leave, and Military Training Leave

New Mexico Independent Community CollegesDental Insurance, Vision Insurance, Basic Life Insurance, Additional Life Insurance, and Long-term

Disability Insurance

North Carolina Community College SystemTuition and Fee Waiver, Sick Leave, Annual Leave, Disability Benefits, Pierce Group Benefits (Insurance

agency), Dental Insurance, Vision Insurance, and Life Insurance

Texas State Technical College SystemEmployee Education Program, Employee Paid-Leave and Holidays, Tuition Assistance, Life Insurance,

Disability Insurance, and Dental Insurance

Virginia Community College System

Life Insurance, Sickness and Disability Program, Vacation Leave, Flexible Reimbursement Accounts,

Holiday Leave, Tax-Sheltered Annuities, Deferred Compensation, Cash Match, Educational Assistance,

Employee Assistance Program, Wellness Program, Worker's Compensation, Virginia College Savings

Plan, Teachers Credit Union, and Discounts Program

SUNY Community College SystemTuition waivers, Dental Insurance, Disability Insurance, Life Insurance, Deductible and Prescription Drug

Co-Payment, Credit Union, and Flexible Spending Plan

Florida Department of EducationTuition waivers, Life, Disability, & Dental Insurance, Vacation & Sabbatical Leave, and Flexible Spending

Plan

Minnesota State Colleges and UniversitiesTime-off Benefits, Dental Insurance, Life Insurance, Disability and Long-term Care Pre-tax Benefits,

Health and Wellness Programs, and Tuition Waivers

2016-17 Results



IPEDS Variables: Definitions and Source Reference

Instructional Staff Instructional staff – total

Variable Description Number of full-time, non-medical, instructional staff - total as of November 1, on 9, 10, 11 or 12 month contract and less less-than 9-month contract. Instructional Staff- An occupational category that consists of the following two functions: 1) "Instruction" only and 2) "Instruction combined with research and/or public service Contract length - The contracted teaching period. The number of months should correspond with the number of months worked (which may differ from the number of months over which staff are paid). Excludes medical staff - Staff employed by or staff working in the medical school (Doctor of Medicine [M.D.] and/or Doctor of Osteopathic Medicine [D.O.]) component of a postsecondary institution or in a free standing medical school. Does not include staff employed by or employees working strictly in a hospital associated with a medical school or those who work in health or allied health schools or departments such as dentistry, veterinary medicine, nursing or dental hygiene unless the health or allied health schools or departments are affiliated with (housed in or under the authority of) the medical school. Salary outlays for instructional staff equated to a 9-month contract-total Variable Description Total salary outlays of full-time, non-medical, instructional staff - total equated to month contract. This is calculated by adjusting the salary outlays at 10-, 11- 12-month contracts to a 9-month contract as follows: (salary outlays at 9-months SA09MOT)+ (.90 * salary outlays at 10 months SA10MOT)+ (.818 *salary outlays at 11 months SA11MOT) + (.75 * salary outlays at 12 months SA12MOT) Instructional Staff- An occupational category that consists of the following two functions: 1) "Instruction" only and 2) "Instruction combined with research and/or public service Annual salary outlays (combined salaries of all staff) include base salaries only – no supplements, overloads or bonuses. Additional stipends for administrative, managerial or other responsibilities are NOT included in the salary outlays data for instructional staff. Contract length – ibid.

Excludes medical staff – ibid.

Non-Medical and Non-Instructional Staff Researchers Variable Description Research - An occupational category used to classify persons whose specific assignments customarily are made for the purpose of conducting research. Regardless of title, academic rank, or tenure status, these employees formally spend the majority of their time conducting research Public Service Variable Description Public Service - An occupational category used to classify persons whose specific assignments customarily are made for the purpose of carrying out public service activities such as agricultural extension services, clinical services, or continuing education. Regardless of title, academic rank, or tenure status, these employees formally spend the majority of their time carrying out public service activities. (This category includes employees with a public service assignment regardless of the location of the assignment (e.g., in the field rather than on campus). Librarians, Curators, Archivists, and Academic Affairs and Other Education Services



Variable Description This variable includes the following four SOC categories Archivists, Curators, and Museum Technicians - An occupational category based on the broad occupation in the 2010 Standard Occupational Classification (SOC) Manual called "Archivists, Curators, and Museum Technicians." For detailed information, refer to the following website: http://www.bls.gov/soc/2010/soc254010.htm. Librarians - An occupational category based on the broad occupation in the 2010 Standard Occupational Classification (SOC) Manual called "Librarians." For detailed information, refer to the following website: http://www.bls.gov/soc/2010/soc254020.htm Library Technicians An occupational category based on the broad occupation in the 2010 Standard Occupational Classification (SOC) Manual called "Library Technicians." For detailed information, refer to the following website: http://www.bls.gov/soc/2010/soc254030.htm. Academic Affairs and Other Education Services Occupations - An occupational category based on the following three minor groups in the 2010 Standard Occupational Classification (SOC) Manual: 1) Pre-school, Primary, Secondary, and Special Education School Teachers (http://www.bls.gov/soc/2010/soc250000.htm#25-2000); 2) Other Teachers and Instructors (http://www.bls.gov/soc/2010/soc250000.htm#25-3000); and 3) Other Education, Training, and Library Occupations (http://www.bls.gov/soc/2010/soc250000.htm#25-9000). Management Variable Description Management Occupations - An occupational category based on the major group in the 2010 Standard Occupational Classification (SOC) Manual called “Management Occupations.” For detailed information refer to the following website: http://www.bls.gov/soc/2010/soc110000.htm. Business and Financial Operations Variable Description Business and Financial Operations Occupations - An occupational category based on the major group in the 2010 Standard Occupational Classification (SOC) Manual called “Business and Financial Operations Occupations.” For detailed information refer to the following website: http://www.bls.gov/soc/2010/soc130000.htm. Computer, Engineering, and Science Variable Description Computer, Engineering, and Science Occupations - An occupational category based on the following three major groups in the 2010 Standard Occupational Classification (SOC) Manual: 1) Computer and Mathematical Occupations (http://www.bls.gov/soc/2010/soc150000.htm); 2) Architecture and Engineering Occupations (http://www.bls.gov/soc/2010/soc170000.htm); and 3) Life, Physical, and Social Science Occupations (http://www.bls.gov/soc/2010/soc190000.htm). Community, Social Service, Legal, Arts, Design, Entertainment, Sports and Media Variable Description Community, Social Service, Legal, Arts, Design, Entertainment, Sports and Media Occupations - An occupational category based on the following three major groups in the 2010 Standard Occupational Classification (SOC) Manual: 1) Community and Social Service Occupations (http://www.bls.gov/soc/2010/soc210000.htm); 2) Legal Occupations (http://www.bls.gov/soc/2010/soc230000.htm); and 3) Arts, Design, Entertainment, Sports, and Media Occupations (http://www.bls.gov/soc/2010/soc270000.htm). Healthcare Practitioners and Technical Variable Description Healthcare Practitioners and Technical Occupations - An occupational category based on the major group in the 2010 Standard Occupational Classification (SOC) Manual called “Healthcare Practitioners and Technical Occupations.” For detailed information refer to the following website: http://www.bls.gov/soc/2010/soc290000.htm Service Variable Description



Service Occupations - An occupational category based on the following five major groups in the 2010 Standard Occupational Classification (SOC) Manual: 1) Healthcare Support Occupations (http://www.bls.gov/soc/2010/soc310000.htm); 2) Protective Service Occupations (http://www.bls.gov/soc/2010/soc330000.htm); 3) Food Preparation and Serving Related Occupations (http://www.bls.gov/soc/2010/soc350000.htm); 4) Building and Grounds Cleaning and Maintenance Occupations (http://www.bls.gov/soc/2010/soc370000.htm); and 5) Personal Care and Service Occupations (http://www.bls.gov/soc/2010/soc390000.htm). Sales and related Variable Description Sales and Related Occupations - An occupational category based on the major group in the 2010 Standard Occupational Classification (SOC) Manual called “Sales and Related Occupations.” For detailed information refer to the following website: http://www.bls.gov/soc/2010/soc410000.htm. Office and Administrative Support Variable Description Office and Administrative Support Occupations - An occupational category based on the major group in the 2010 Standard Occupational Classification (SOC) Manual called “Office and Administrative Support Occupations.” For detailed information refer to the following website: http://www.bls.gov/soc/2010/soc430000.htm. Natural Resources, Construction, and Maintenance Variable Description Natural Resources, Construction, and Maintenance Occupations - An occupational category based on the following three major groups in the 2010 Standard Occupational Classification (SOC) Manual: 1) Farming, Fishing, and Forestry Occupations (http://www.bls.gov/soc/2010/soc450000.htm); 2) Construction and Extraction Occupations (http://www.bls.gov/soc/2010/soc470000.htm); and 3) Installation, Maintenance, and Repair Occupations (http://www.bls.gov/soc/2010/soc490000.htm). Production, Transportation, and Material Moving Variable Description Production, Transportation, and Material Moving Occupations - An occupational category based on the following two major

groups in the 2010 Standard Occupational Classification (SOC) Manual: 1) Production Occupations

(http://www.bls.gov/soc/2010/soc510000.htm) and 2) Transportation and Material Moving Occupations

(http://www.bls.gov/soc/2010/soc530000.htm).

Documents

Washington State Community and Technical Colleges: Faculty ... SBCTC Salary Study.pdf · colleges to recruit and retain high caliber faculty and administrative staff. In this report